skill
manuscript-argument-compass
A cross-platform Agent Skill for keeping one evidence-bounded argument coherent across an academic manuscript.
Set up
Skills are installed by placing their folder where your agent loads skills.
The download comes from github.com/chaosbeagle/manuscript-argument-compass itself, not from our server, so what you get is exactly what its author published.
Install instructions come from the README
README
synced Oct 22, 2026Manuscript Argument Compass
Manuscript Argument Compass is a reusable Agent Skill for keeping one evidence-bounded argument coherent across an academic manuscript.
The included skill, steer-question-driven-manuscript, operates at the
meta-writing level. It identifies the paper's latent governing inquiry and
internal governing proposition, checks paper identity across sections and
displays, maps claims to evidence, and hands a stable argument compass to
specialised drafting or reporting skills.
It does not replace domain-specific writing, reporting, statistics, citation, or venue guidance.
What it helps with
- diagnosing drift between Introduction, Methods, Results, Discussion, and Conclusion;
- separating the paper's primary argument from attractive secondary branches;
- checking whether the design can adjudicate the intended claims;
- organising Results around the governing inquiry rather than experiment chronology;
- auditing the reviewer-first path through Title, Abstract, displays, Conclusion, Methods, and Introduction;
- designing figures and tables as evidence-bearing argument units;
- keeping alternative explanations distinct from scope or design limitations;
- calibrating claim strength without making the manuscript defensive.
Repository layout
skills/
└── steer-question-driven-manuscript/
├── SKILL.md
├── agents/
│ └── openai.yaml
└── references/
├── introduction-argument.md
├── venue-modes.md
└── evidence-integrity.md
The skill content has one canonical source. Codex and Claude Code use the same
SKILL.md and references; only installation and invocation differ.
Install for Codex
Copy skills/steer-question-driven-manuscript/ into:
$CODEX_HOME/skills/whenCODEX_HOMEis configured; or~/.codex/skills/otherwise.
Start a new task after first installation. Invoke it explicitly with:
Use $steer-question-driven-manuscript to identify this paper's governing
inquiry, run the Level 0 identity checksum, and prepare a handoff for the most
appropriate academic-writing skill.
The agents/openai.yaml file provides Codex/OpenAI interface metadata.
Install for Claude Code
For a personal skill available across projects, copy the same directory into:
~/.claude/skills/steer-question-driven-manuscript/
For a project-scoped installation, copy it into:
.claude/skills/steer-question-driven-manuscript/
Invoke it with:
/steer-question-driven-manuscript Audit this manuscript's governing
proposition, evidence chain, visual argument, and section-level drift.
Claude Code reads SKILL.md and its referenced resources. It does not require
the Codex-specific agents/openai.yaml.
Suggested workflow
- Supply the manuscript or available artifacts, target venue, and article type.
- Let the skill reconstruct the latent inquiry rather than forcing a polished research-question sentence.
- Review the governing proposition and Level 0 checksum.
- Resolve evidence gaps or paper-identity mismatches before sentence editing.
- Hand the compass to a specialised writing skill.
- Re-audit the finished draft for argument drift and unsupported upgrades.
Scope and evidence boundary
The skill can organise supplied evidence and expose contradictions. It must not invent facts, citations, methods, results, uncertainty estimates, or generalisability. Authors remain responsible for scientific verification and the final manuscript.
Acknowledgement
The initial motivation for the Introduction component was influenced by Shih-Hsin Wang's article, 如何寫出一個有說服力的 Introduction. The present repository develops an independently worded, manuscript-wide framework. See NOTICE.md for provenance details.
License
Original material in this repository is licensed under the Creative Commons Attribution 4.0 International License.